Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations2454
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory649.8 B

Variable types

DateTime1
Categorical6
Numeric9
Text2

Alerts

Activity is highly overall correlated with calories and 2 other fieldsHigh correlation
DayType is highly overall correlated with Day_NameHigh correlation
Day_Name is highly overall correlated with DayTypeHigh correlation
Full_date (Month Index) is highly overall correlated with Full_date (Month) and 2 other fieldsHigh correlation
Full_date (Month) is highly overall correlated with Full_date (Month Index) and 2 other fieldsHigh correlation
Full_date (Quarter) is highly overall correlated with Full_date (Month Index) and 2 other fieldsHigh correlation
Month Name is highly overall correlated with Full_date (Month Index) and 2 other fieldsHigh correlation
calories is highly overall correlated with Activity and 3 other fieldsHigh correlation
deepSleepTime is highly overall correlated with shallowSleepTimeHigh correlation
distance is highly overall correlated with Activity and 3 other fieldsHigh correlation
runDistance is highly overall correlated with calories and 2 other fieldsHigh correlation
shallowSleepTime is highly overall correlated with deepSleepTimeHigh correlation
steps is highly overall correlated with Activity and 3 other fieldsHigh correlation
Day_Name is uniformly distributed Uniform
Full_date has unique values Unique
steps has 37 (1.5%) zeros Zeros
distance has 37 (1.5%) zeros Zeros
runDistance has 108 (4.4%) zeros Zeros
calories has 37 (1.5%) zeros Zeros
deepSleepTime has 454 (18.5%) zeros Zeros
shallowSleepTime has 449 (18.3%) zeros Zeros
wakeTime has 2013 (82.0%) zeros Zeros

Reproduction

Analysis started2025-03-31 00:20:39.756706
Analysis finished2025-03-31 00:21:08.615069
Duration28.86 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Full_date
Date

Unique 

Distinct2454
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size19.3 KiB
Minimum2016-04-27 00:00:00
Maximum2023-01-14 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-30T21:21:09.089619image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:21:09.412144image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Day_Name
Categorical

High correlation  Uniform 

Distinct7
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size153.8 KiB
Wednesday
351 
Friday
351 
Saturday
351 
Thursday
351 
Sunday
350 
Other values (2)
700 

Length

Max length9
Median length8
Mean length7.1438468
Min length6

Characters and Unicode

Total characters17531
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSunday
2nd rowSunday
3rd rowMonday
4th rowWednesday
5th rowFriday

Common Values

ValueCountFrequency (%)
Wednesday 351
14.3%
Friday 351
14.3%
Saturday 351
14.3%
Thursday 351
14.3%
Sunday 350
14.3%
Monday 350
14.3%
Tuesday 350
14.3%

Length

2025-03-30T21:21:09.769163image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-30T21:21:10.099036image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
wednesday 351
14.3%
friday 351
14.3%
saturday 351
14.3%
thursday 351
14.3%
sunday 350
14.3%
monday 350
14.3%
tuesday 350
14.3%

Most occurring characters

ValueCountFrequency (%)
d 2805
16.0%
a 2805
16.0%
y 2454
14.0%
u 1402
8.0%
r 1053
 
6.0%
s 1052
 
6.0%
e 1052
 
6.0%
n 1051
 
6.0%
T 701
 
4.0%
S 701
 
4.0%
Other values (7) 2455
14.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17531
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 2805
16.0%
a 2805
16.0%
y 2454
14.0%
u 1402
8.0%
r 1053
 
6.0%
s 1052
 
6.0%
e 1052
 
6.0%
n 1051
 
6.0%
T 701
 
4.0%
S 701
 
4.0%
Other values (7) 2455
14.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17531
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 2805
16.0%
a 2805
16.0%
y 2454
14.0%
u 1402
8.0%
r 1053
 
6.0%
s 1052
 
6.0%
e 1052
 
6.0%
n 1051
 
6.0%
T 701
 
4.0%
S 701
 
4.0%
Other values (7) 2455
14.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17531
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 2805
16.0%
a 2805
16.0%
y 2454
14.0%
u 1402
8.0%
r 1053
 
6.0%
s 1052
 
6.0%
e 1052
 
6.0%
n 1051
 
6.0%
T 701
 
4.0%
S 701
 
4.0%
Other values (7) 2455
14.0%

steps
Real number (ℝ)

High correlation  Zeros 

Distinct2280
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8262.9055
Minimum0
Maximum38443
Zeros37
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size19.3 KiB
2025-03-30T21:21:10.502739image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile591.55
Q13225
median6983
Q311046
95-th percentile22080.15
Maximum38443
Range38443
Interquartile range (IQR)7821

Descriptive statistics

Standard deviation6666.92
Coefficient of variation (CV)0.80684936
Kurtosis1.9883087
Mean8262.9055
Median Absolute Deviation (MAD)3878
Skewness1.3373198
Sum20277170
Variance44447822
MonotonicityNot monotonic
2025-03-30T21:21:10.935123image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 37
 
1.5%
5282 3
 
0.1%
2269 3
 
0.1%
7053 3
 
0.1%
7143 3
 
0.1%
4924 3
 
0.1%
5054 3
 
0.1%
1528 3
 
0.1%
1407 3
 
0.1%
7766 3
 
0.1%
Other values (2270) 2390
97.4%
ValueCountFrequency (%)
0 37
1.5%
12 1
 
< 0.1%
14 2
 
0.1%
24 1
 
< 0.1%
28 1
 
< 0.1%
34 1
 
< 0.1%
43 1
 
< 0.1%
53 1
 
< 0.1%
55 1
 
< 0.1%
63 1
 
< 0.1%
ValueCountFrequency (%)
38443 1
< 0.1%
37375 1
< 0.1%
37323 1
< 0.1%
37255 1
< 0.1%
37126 1
< 0.1%
36537 1
< 0.1%
36318 1
< 0.1%
36135 1
< 0.1%
34543 1
< 0.1%
33213 1
< 0.1%

Activity
Categorical

High correlation 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size148.3 KiB
Low
948 
High
765 
Moderate
741 

Length

Max length8
Median length4
Mean length4.8215159
Min length3

Characters and Unicode

Total characters11832
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLow
2nd rowLow
3rd rowLow
4th rowLow
5th rowLow

Common Values

ValueCountFrequency (%)
Low 948
38.6%
High 765
31.2%
Moderate 741
30.2%

Length

2025-03-30T21:21:11.323948image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-30T21:21:11.582748image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
low 948
38.6%
high 765
31.2%
moderate 741
30.2%

Most occurring characters

ValueCountFrequency (%)
o 1689
14.3%
e 1482
12.5%
L 948
8.0%
w 948
8.0%
H 765
 
6.5%
i 765
 
6.5%
g 765
 
6.5%
h 765
 
6.5%
M 741
 
6.3%
d 741
 
6.3%
Other values (3) 2223
18.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11832
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1689
14.3%
e 1482
12.5%
L 948
8.0%
w 948
8.0%
H 765
 
6.5%
i 765
 
6.5%
g 765
 
6.5%
h 765
 
6.5%
M 741
 
6.3%
d 741
 
6.3%
Other values (3) 2223
18.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11832
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1689
14.3%
e 1482
12.5%
L 948
8.0%
w 948
8.0%
H 765
 
6.5%
i 765
 
6.5%
g 765
 
6.5%
h 765
 
6.5%
M 741
 
6.3%
d 741
 
6.3%
Other values (3) 2223
18.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11832
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1689
14.3%
e 1482
12.5%
L 948
8.0%
w 948
8.0%
H 765
 
6.5%
i 765
 
6.5%
g 765
 
6.5%
h 765
 
6.5%
M 741
 
6.3%
d 741
 
6.3%
Other values (3) 2223
18.8%

distance
Real number (ℝ)

High correlation  Zeros 

Distinct2216
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5955.6125
Minimum0
Maximum29485
Zeros37
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size19.3 KiB
2025-03-30T21:21:11.920824image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile384
Q12227
median5010
Q37994
95-th percentile16036.6
Maximum29485
Range29485
Interquartile range (IQR)5767

Descriptive statistics

Standard deviation4872.125
Coefficient of variation (CV)0.81807288
Kurtosis2.0210656
Mean5955.6125
Median Absolute Deviation (MAD)2871.5
Skewness1.3375923
Sum14615073
Variance23737602
MonotonicityNot monotonic
2025-03-30T21:21:12.325106image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 37
 
1.5%
3152 3
 
0.1%
2245 3
 
0.1%
5991 3
 
0.1%
2225 3
 
0.1%
2344 3
 
0.1%
343 3
 
0.1%
2651 3
 
0.1%
3314 3
 
0.1%
3137 3
 
0.1%
Other values (2206) 2390
97.4%
ValueCountFrequency (%)
0 37
1.5%
8 1
 
< 0.1%
9 2
 
0.1%
16 1
 
< 0.1%
18 1
 
< 0.1%
22 1
 
< 0.1%
28 1
 
< 0.1%
34 1
 
< 0.1%
36 1
 
< 0.1%
41 1
 
< 0.1%
ValueCountFrequency (%)
29485 1
< 0.1%
28234 1
< 0.1%
27868 1
< 0.1%
27296 1
< 0.1%
26544 1
< 0.1%
26491 1
< 0.1%
26240 1
< 0.1%
26057 1
< 0.1%
24713 1
< 0.1%
24523 1
< 0.1%

runDistance
Real number (ℝ)

High correlation  Zeros 

Distinct1067
Distinct (%)43.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean826.98655
Minimum0
Maximum21952
Zeros108
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size19.3 KiB
2025-03-30T21:21:12.732432image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile22.65
Q1153
median296
Q3600
95-th percentile3723.1
Maximum21952
Range21952
Interquartile range (IQR)447

Descriptive statistics

Standard deviation1826.378
Coefficient of variation (CV)2.2084736
Kurtosis35.022073
Mean826.98655
Median Absolute Deviation (MAD)177
Skewness5.266571
Sum2029425
Variance3335656.6
MonotonicityNot monotonic
2025-03-30T21:21:13.142764image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 108
 
4.4%
170 12
 
0.5%
126 12
 
0.5%
309 10
 
0.4%
166 10
 
0.4%
256 10
 
0.4%
121 9
 
0.4%
177 9
 
0.4%
305 9
 
0.4%
167 9
 
0.4%
Other values (1057) 2256
91.9%
ValueCountFrequency (%)
0 108
4.4%
7 2
 
0.1%
8 1
 
< 0.1%
9 2
 
0.1%
11 2
 
0.1%
15 2
 
0.1%
18 1
 
< 0.1%
19 1
 
< 0.1%
22 4
 
0.2%
23 2
 
0.1%
ValueCountFrequency (%)
21952 1
< 0.1%
17890 1
< 0.1%
17564 1
< 0.1%
16669 1
< 0.1%
16631 1
< 0.1%
15784 1
< 0.1%
15419 1
< 0.1%
14547 1
< 0.1%
14496 1
< 0.1%
14348 1
< 0.1%

calories
Real number (ℝ)

High correlation  Zeros 

Distinct676
Distinct (%)27.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean240.00815
Minimum0
Maximum2543
Zeros37
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size19.3 KiB
2025-03-30T21:21:13.542068image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19
Q190
median190
Q3308.75
95-th percentile665.35
Maximum2543
Range2543
Interquartile range (IQR)218.75

Descriptive statistics

Standard deviation213.67833
Coefficient of variation (CV)0.89029615
Kurtosis8.6145989
Mean240.00815
Median Absolute Deviation (MAD)106
Skewness2.0976524
Sum588980
Variance45658.43
MonotonicityNot monotonic
2025-03-30T21:21:13.972165image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 37
 
1.5%
198 16
 
0.7%
88 13
 
0.5%
67 13
 
0.5%
66 12
 
0.5%
248 12
 
0.5%
174 12
 
0.5%
70 11
 
0.4%
53 11
 
0.4%
107 11
 
0.4%
Other values (666) 2306
94.0%
ValueCountFrequency (%)
0 37
1.5%
1 6
 
0.2%
2 4
 
0.2%
4 3
 
0.1%
5 1
 
< 0.1%
6 2
 
0.1%
7 5
 
0.2%
8 1
 
< 0.1%
9 5
 
0.2%
10 2
 
0.1%
ValueCountFrequency (%)
2543 1
< 0.1%
1358 1
< 0.1%
1333 1
< 0.1%
1320 1
< 0.1%
1311 1
< 0.1%
1197 1
< 0.1%
1195 1
< 0.1%
1171 1
< 0.1%
1168 1
< 0.1%
1160 1
< 0.1%

Year
Real number (ℝ)

Distinct8
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.165
Minimum2016
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.3 KiB
2025-03-30T21:21:14.300306image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2016
Q12017.25
median2019
Q32021
95-th percentile2022
Maximum2023
Range7
Interquartile range (IQR)3.75

Descriptive statistics

Standard deviation1.9506927
Coefficient of variation (CV)0.00096608877
Kurtosis-1.1804368
Mean2019.165
Median Absolute Deviation (MAD)2
Skewness-0.033169867
Sum4955031
Variance3.8052019
MonotonicityNot monotonic
2025-03-30T21:21:14.607619image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2020 366
14.9%
2017 365
14.9%
2018 365
14.9%
2019 365
14.9%
2021 365
14.9%
2022 365
14.9%
2016 249
10.1%
2023 14
 
0.6%
ValueCountFrequency (%)
2016 249
10.1%
2017 365
14.9%
2018 365
14.9%
2019 365
14.9%
2020 366
14.9%
2021 365
14.9%
2022 365
14.9%
2023 14
 
0.6%
ValueCountFrequency (%)
2023 14
 
0.6%
2022 365
14.9%
2021 365
14.9%
2020 366
14.9%
2019 365
14.9%
2018 365
14.9%
2017 365
14.9%
2016 249
10.1%

Month Name
Categorical

High correlation 

Distinct12
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size151.5 KiB
May
217 
July
217 
August
217 
October
217 
December
217 
Other values (7)
1369 

Length

Max length9
Median length7
Mean length6.1483293
Min length3

Characters and Unicode

Total characters15088
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMay
2nd rowMay
3rd rowMay
4th rowMay
5th rowMay

Common Values

ValueCountFrequency (%)
May 217
8.8%
July 217
8.8%
August 217
8.8%
October 217
8.8%
December 217
8.8%
June 210
8.6%
September 210
8.6%
November 210
8.6%
January 200
8.1%
March 186
7.6%
Other values (2) 353
14.4%

Length

2025-03-30T21:21:15.013974image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
may 217
8.8%
july 217
8.8%
august 217
8.8%
october 217
8.8%
december 217
8.8%
june 210
8.6%
september 210
8.6%
november 210
8.6%
january 200
8.1%
march 186
7.6%
Other values (2) 353
14.4%

Most occurring characters

ValueCountFrequency (%)
e 2297
15.2%
r 1762
 
11.7%
u 1230
 
8.2%
b 1023
 
6.8%
a 972
 
6.4%
y 803
 
5.3%
t 644
 
4.3%
m 637
 
4.2%
J 627
 
4.2%
c 620
 
4.1%
Other values (16) 4473
29.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15088
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2297
15.2%
r 1762
 
11.7%
u 1230
 
8.2%
b 1023
 
6.8%
a 972
 
6.4%
y 803
 
5.3%
t 644
 
4.3%
m 637
 
4.2%
J 627
 
4.2%
c 620
 
4.1%
Other values (16) 4473
29.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15088
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2297
15.2%
r 1762
 
11.7%
u 1230
 
8.2%
b 1023
 
6.8%
a 972
 
6.4%
y 803
 
5.3%
t 644
 
4.3%
m 637
 
4.2%
J 627
 
4.2%
c 620
 
4.1%
Other values (16) 4473
29.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15088
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2297
15.2%
r 1762
 
11.7%
u 1230
 
8.2%
b 1023
 
6.8%
a 972
 
6.4%
y 803
 
5.3%
t 644
 
4.3%
m 637
 
4.2%
J 627
 
4.2%
c 620
 
4.1%
Other values (16) 4473
29.6%

DayType
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size153.5 KiB
Weekday
1753 
Weekend
701 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters17178
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWeekend
2nd rowWeekend
3rd rowWeekday
4th rowWeekday
5th rowWeekday

Common Values

ValueCountFrequency (%)
Weekday 1753
71.4%
Weekend 701
 
28.6%

Length

2025-03-30T21:21:15.356801image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-30T21:21:15.620420image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
weekday 1753
71.4%
weekend 701
 
28.6%

Most occurring characters

ValueCountFrequency (%)
e 5609
32.7%
W 2454
14.3%
k 2454
14.3%
d 2454
14.3%
a 1753
 
10.2%
y 1753
 
10.2%
n 701
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17178
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 5609
32.7%
W 2454
14.3%
k 2454
14.3%
d 2454
14.3%
a 1753
 
10.2%
y 1753
 
10.2%
n 701
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17178
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 5609
32.7%
W 2454
14.3%
k 2454
14.3%
d 2454
14.3%
a 1753
 
10.2%
y 1753
 
10.2%
n 701
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17178
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 5609
32.7%
W 2454
14.3%
k 2454
14.3%
d 2454
14.3%
a 1753
 
10.2%
y 1753
 
10.2%
n 701
 
4.1%

Full_date (Quarter)
Categorical

High correlation 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size146.3 KiB
Qtr3
644 
Qtr4
644 
Qtr2
611 
Qtr1
555 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters9816
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowQtr2
2nd rowQtr2
3rd rowQtr2
4th rowQtr2
5th rowQtr2

Common Values

ValueCountFrequency (%)
Qtr3 644
26.2%
Qtr4 644
26.2%
Qtr2 611
24.9%
Qtr1 555
22.6%

Length

2025-03-30T21:21:15.895178image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-30T21:21:16.209687image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
qtr3 644
26.2%
qtr4 644
26.2%
qtr2 611
24.9%
qtr1 555
22.6%

Most occurring characters

ValueCountFrequency (%)
Q 2454
25.0%
t 2454
25.0%
r 2454
25.0%
3 644
 
6.6%
4 644
 
6.6%
2 611
 
6.2%
1 555
 
5.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9816
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Q 2454
25.0%
t 2454
25.0%
r 2454
25.0%
3 644
 
6.6%
4 644
 
6.6%
2 611
 
6.2%
1 555
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9816
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Q 2454
25.0%
t 2454
25.0%
r 2454
25.0%
3 644
 
6.6%
4 644
 
6.6%
2 611
 
6.2%
1 555
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9816
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Q 2454
25.0%
t 2454
25.0%
r 2454
25.0%
3 644
 
6.6%
4 644
 
6.6%
2 611
 
6.2%
1 555
 
5.7%

Full_date (Month Index)
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6854116
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.3 KiB
2025-03-30T21:21:16.510119image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4197538
Coefficient of variation (CV)0.51152479
Kurtosis-1.1664684
Mean6.6854116
Median Absolute Deviation (MAD)3
Skewness-0.078264022
Sum16406
Variance11.694716
MonotonicityNot monotonic
2025-03-30T21:21:16.819421image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
5 217
8.8%
7 217
8.8%
8 217
8.8%
10 217
8.8%
12 217
8.8%
6 210
8.6%
9 210
8.6%
11 210
8.6%
1 200
8.1%
3 186
7.6%
Other values (2) 353
14.4%
ValueCountFrequency (%)
1 200
8.1%
2 169
6.9%
3 186
7.6%
4 184
7.5%
5 217
8.8%
6 210
8.6%
7 217
8.8%
8 217
8.8%
9 210
8.6%
10 217
8.8%
ValueCountFrequency (%)
12 217
8.8%
11 210
8.6%
10 217
8.8%
9 210
8.6%
8 217
8.8%
7 217
8.8%
6 210
8.6%
5 217
8.8%
4 184
7.5%
3 186
7.6%

Full_date (Month)
Categorical

High correlation 

Distinct12
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size143.9 KiB
May
217 
Jul
217 
Aug
217 
Oct
217 
Dec
217 
Other values (7)
1369 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7362
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMay
2nd rowMay
3rd rowMay
4th rowMay
5th rowMay

Common Values

ValueCountFrequency (%)
May 217
8.8%
Jul 217
8.8%
Aug 217
8.8%
Oct 217
8.8%
Dec 217
8.8%
Jun 210
8.6%
Sep 210
8.6%
Nov 210
8.6%
Jan 200
8.1%
Mar 186
7.6%
Other values (2) 353
14.4%

Length

2025-03-30T21:21:17.154846image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
may 217
8.8%
jul 217
8.8%
aug 217
8.8%
oct 217
8.8%
dec 217
8.8%
jun 210
8.6%
sep 210
8.6%
nov 210
8.6%
jan 200
8.1%
mar 186
7.6%
Other values (2) 353
14.4%

Most occurring characters

ValueCountFrequency (%)
u 644
 
8.7%
J 627
 
8.5%
a 603
 
8.2%
e 596
 
8.1%
c 434
 
5.9%
n 410
 
5.6%
M 403
 
5.5%
A 401
 
5.4%
p 394
 
5.4%
r 370
 
5.0%
Other values (12) 2480
33.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7362
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u 644
 
8.7%
J 627
 
8.5%
a 603
 
8.2%
e 596
 
8.1%
c 434
 
5.9%
n 410
 
5.6%
M 403
 
5.5%
A 401
 
5.4%
p 394
 
5.4%
r 370
 
5.0%
Other values (12) 2480
33.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7362
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u 644
 
8.7%
J 627
 
8.5%
a 603
 
8.2%
e 596
 
8.1%
c 434
 
5.9%
n 410
 
5.6%
M 403
 
5.5%
A 401
 
5.4%
p 394
 
5.4%
r 370
 
5.0%
Other values (12) 2480
33.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7362
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u 644
 
8.7%
J 627
 
8.5%
a 603
 
8.2%
e 596
 
8.1%
c 434
 
5.9%
n 410
 
5.6%
M 403
 
5.5%
A 401
 
5.4%
p 394
 
5.4%
r 370
 
5.0%
Other values (12) 2480
33.7%

deepSleepTime
Real number (ℝ)

High correlation  Zeros 

Distinct279
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean106.5379
Minimum0
Maximum384
Zeros454
Zeros (%)18.5%
Negative0
Negative (%)0.0%
Memory size19.3 KiB
2025-03-30T21:21:17.488094image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q158
median107
Q3157
95-th percentile222
Maximum384
Range384
Interquartile range (IQR)99

Descriptive statistics

Standard deviation72.531236
Coefficient of variation (CV)0.68080221
Kurtosis-0.41334533
Mean106.5379
Median Absolute Deviation (MAD)50
Skewness0.19523357
Sum261444
Variance5260.7803
MonotonicityNot monotonic
2025-03-30T21:21:17.889227image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 454
 
18.5%
108 19
 
0.8%
140 18
 
0.7%
149 18
 
0.7%
86 18
 
0.7%
68 17
 
0.7%
139 17
 
0.7%
133 17
 
0.7%
69 17
 
0.7%
101 17
 
0.7%
Other values (269) 1842
75.1%
ValueCountFrequency (%)
0 454
18.5%
4 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
12 1
 
< 0.1%
14 1
 
< 0.1%
15 1
 
< 0.1%
16 1
 
< 0.1%
17 1
 
< 0.1%
20 2
 
0.1%
ValueCountFrequency (%)
384 1
< 0.1%
365 1
< 0.1%
346 1
< 0.1%
343 1
< 0.1%
339 1
< 0.1%
335 1
< 0.1%
326 1
< 0.1%
318 1
< 0.1%
314 1
< 0.1%
304 2
0.1%

shallowSleepTime
Real number (ℝ)

High correlation  Zeros 

Distinct377
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean234.84963
Minimum0
Maximum601
Zeros449
Zeros (%)18.3%
Negative0
Negative (%)0.0%
Memory size19.3 KiB
2025-03-30T21:21:18.289420image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1187
median262
Q3323
95-th percentile412
Maximum601
Range601
Interquartile range (IQR)136

Descriptive statistics

Standard deviation132.16658
Coefficient of variation (CV)0.56277106
Kurtosis-0.38745821
Mean234.84963
Median Absolute Deviation (MAD)66
Skewness-0.57858594
Sum576321
Variance17468.004
MonotonicityNot monotonic
2025-03-30T21:21:18.676062image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 449
 
18.3%
251 21
 
0.9%
288 20
 
0.8%
263 19
 
0.8%
259 19
 
0.8%
282 17
 
0.7%
290 16
 
0.7%
257 16
 
0.7%
286 15
 
0.6%
264 15
 
0.6%
Other values (367) 1847
75.3%
ValueCountFrequency (%)
0 449
18.3%
21 1
 
< 0.1%
22 1
 
< 0.1%
23 1
 
< 0.1%
29 1
 
< 0.1%
32 1
 
< 0.1%
41 1
 
< 0.1%
52 1
 
< 0.1%
53 1
 
< 0.1%
54 1
 
< 0.1%
ValueCountFrequency (%)
601 1
< 0.1%
596 1
< 0.1%
595 1
< 0.1%
586 1
< 0.1%
583 1
< 0.1%
578 1
< 0.1%
574 1
< 0.1%
551 1
< 0.1%
549 1
< 0.1%
548 1
< 0.1%

wakeTime
Real number (ℝ)

Zeros 

Distinct86
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.307661
Minimum0
Maximum604
Zeros2013
Zeros (%)82.0%
Negative0
Negative (%)0.0%
Memory size19.3 KiB
2025-03-30T21:21:19.274905image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile21.35
Maximum604
Range604
Interquartile range (IQR)0

Descriptive statistics

Standard deviation24.557135
Coefficient of variation (CV)5.700805
Kurtosis234.78192
Mean4.307661
Median Absolute Deviation (MAD)0
Skewness13.131395
Sum10571
Variance603.05288
MonotonicityNot monotonic
2025-03-30T21:21:19.637172image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2013
82.0%
1 81
 
3.3%
2 77
 
3.1%
3 36
 
1.5%
4 20
 
0.8%
6 14
 
0.6%
11 11
 
0.4%
17 10
 
0.4%
5 9
 
0.4%
33 8
 
0.3%
Other values (76) 175
 
7.1%
ValueCountFrequency (%)
0 2013
82.0%
1 81
 
3.3%
2 77
 
3.1%
3 36
 
1.5%
4 20
 
0.8%
5 9
 
0.4%
6 14
 
0.6%
7 7
 
0.3%
8 4
 
0.2%
9 8
 
0.3%
ValueCountFrequency (%)
604 1
< 0.1%
442 1
< 0.1%
371 1
< 0.1%
302 1
< 0.1%
255 1
< 0.1%
242 1
< 0.1%
233 1
< 0.1%
222 1
< 0.1%
218 1
< 0.1%
193 1
< 0.1%

start
Text

Distinct2426
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size165.4 KiB
2025-03-30T21:21:20.457134image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length24
Median length10
Mean length11.947433
Min length1

Characters and Unicode

Total characters29319
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2425 ?
Unique (%)98.8%

Sample

1st row1461708000
2nd row1461801240
3rd row1461883500
4th row1461979500
5th row1462065840
ValueCountFrequency (%)
0 29
 
1.0%
23:00:00+0000 17
 
0.6%
22:00:00+0000 12
 
0.4%
18:00:00+0000 6
 
0.2%
01:14:00+0000 4
 
0.1%
02:15:00+0000 4
 
0.1%
00:35:00+0000 3
 
0.1%
00:25:00+0000 3
 
0.1%
00:02:00+0000 3
 
0.1%
00:28:00+0000 3
 
0.1%
Other values (2595) 2730
97.0%
2025-03-30T21:21:21.685149image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 7464
25.5%
1 4023
13.7%
2 3076
10.5%
5 2484
 
8.5%
6 2184
 
7.4%
4 2133
 
7.3%
8 1799
 
6.1%
3 1356
 
4.6%
9 1343
 
4.6%
7 1297
 
4.4%
Other values (4) 2160
 
7.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 29319
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7464
25.5%
1 4023
13.7%
2 3076
10.5%
5 2484
 
8.5%
6 2184
 
7.4%
4 2133
 
7.3%
8 1799
 
6.1%
3 1356
 
4.6%
9 1343
 
4.6%
7 1297
 
4.4%
Other values (4) 2160
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 29319
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7464
25.5%
1 4023
13.7%
2 3076
10.5%
5 2484
 
8.5%
6 2184
 
7.4%
4 2133
 
7.3%
8 1799
 
6.1%
3 1356
 
4.6%
9 1343
 
4.6%
7 1297
 
4.4%
Other values (4) 2160
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 29319
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7464
25.5%
1 4023
13.7%
2 3076
10.5%
5 2484
 
8.5%
6 2184
 
7.4%
4 2133
 
7.3%
8 1799
 
6.1%
3 1356
 
4.6%
9 1343
 
4.6%
7 1297
 
4.4%
Other values (4) 2160
 
7.4%

stop
Text

Distinct2426
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size165.4 KiB
2025-03-30T21:21:22.414209image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length24
Median length10
Mean length11.947433
Min length1

Characters and Unicode

Total characters29319
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2425 ?
Unique (%)98.8%

Sample

1st row1461708000
2nd row1461826560
3rd row1461912000
4th row1462012920
5th row1462096800
ValueCountFrequency (%)
0 29
 
1.0%
23:00:00+0000 17
 
0.6%
22:00:00+0000 12
 
0.4%
06:57:00+0000 5
 
0.2%
06:46:00+0000 4
 
0.1%
11:13:00+0000 3
 
0.1%
07:31:00+0000 3
 
0.1%
09:04:00+0000 3
 
0.1%
07:53:00+0000 3
 
0.1%
10:43:00+0000 3
 
0.1%
Other values (2644) 2732
97.1%
2025-03-30T21:21:23.540308image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 7575
25.8%
1 3993
13.6%
2 2920
 
10.0%
5 2485
 
8.5%
6 2235
 
7.6%
4 2172
 
7.4%
8 1790
 
6.1%
9 1385
 
4.7%
7 1315
 
4.5%
3 1289
 
4.4%
Other values (4) 2160
 
7.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 29319
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7575
25.8%
1 3993
13.6%
2 2920
 
10.0%
5 2485
 
8.5%
6 2235
 
7.6%
4 2172
 
7.4%
8 1790
 
6.1%
9 1385
 
4.7%
7 1315
 
4.5%
3 1289
 
4.4%
Other values (4) 2160
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 29319
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7575
25.8%
1 3993
13.6%
2 2920
 
10.0%
5 2485
 
8.5%
6 2235
 
7.6%
4 2172
 
7.4%
8 1790
 
6.1%
9 1385
 
4.7%
7 1315
 
4.5%
3 1289
 
4.4%
Other values (4) 2160
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 29319
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7575
25.8%
1 3993
13.6%
2 2920
 
10.0%
5 2485
 
8.5%
6 2235
 
7.6%
4 2172
 
7.4%
8 1790
 
6.1%
9 1385
 
4.7%
7 1315
 
4.5%
3 1289
 
4.4%
Other values (4) 2160
 
7.4%

Interactions

2025-03-30T21:21:04.965061image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:43.049328image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:46.267723image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:49.074824image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:51.591678image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:54.115677image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:56.908350image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:59.710251image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:21:02.209016image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:21:05.360450image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:43.454690image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:46.672646image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:49.348944image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:51.916363image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:54.443924image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:57.232523image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:21:00.006197image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:21:02.494651image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:21:05.645515image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:43.751011image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:46.984077image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:49.639404image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:52.279445image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:54.731564image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:57.738749image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:21:00.291122image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:21:02.762594image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:21:05.896739image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:44.272271image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:47.265164image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:49.923834image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:52.545644image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:55.011201image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:58.025814image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:21:00.554171image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:21:03.037024image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:21:06.150199image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:44.598208image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:47.620922image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:50.195338image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:52.795303image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:55.294080image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:58.270420image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:21:00.830507image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:21:03.318318image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:21:06.428142image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:44.918866image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:47.989373image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:50.543051image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:53.048912image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:55.641050image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:58.600048image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:21:01.115303image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:21:03.662730image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:21:06.694342image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:45.272468image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:48.245707image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:50.811687image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:53.301001image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:55.920565image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:58.891455image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:21:01.396534image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:21:03.976048image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:21:06.976538image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:45.695317image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:48.540411image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:51.080676image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:53.596971image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:56.216834image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:59.196206image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:21:01.688297image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:21:04.303097image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:21:07.221572image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:45.966302image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:48.797315image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:51.352596image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:53.840621image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:56.601998image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:20:59.452449image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:21:01.950868image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T21:21:04.622801image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2025-03-30T21:21:23.884076image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ActivityDayTypeDay_NameFull_date (Month Index)Full_date (Month)Full_date (Quarter)Month NameYearcaloriesdeepSleepTimedistancerunDistanceshallowSleepTimestepswakeTime
Activity1.0000.0230.0000.0740.0690.0380.0690.2520.5950.4500.8300.1700.2050.8150.060
DayType0.0231.0000.9990.0000.0000.0000.0000.0000.0590.0790.0730.0700.0640.0840.000
Day_Name0.0000.9991.0000.0000.0000.0000.0000.0000.0000.0660.0100.0400.0610.0200.022
Full_date (Month Index)0.0740.0000.0001.0001.0000.9991.000-0.1030.024-0.061-0.0050.103-0.054-0.0050.007
Full_date (Month)0.0690.0000.0001.0001.0000.9981.0000.1040.0680.0290.0470.0520.0240.0480.000
Full_date (Quarter)0.0380.0000.0000.9990.9981.0000.9980.1230.0990.0220.0500.0910.0000.0580.007
Month Name0.0690.0000.0001.0001.0000.9981.0000.1040.0680.0290.0470.0520.0240.0480.000
Year0.2520.0000.000-0.1030.1040.1230.1041.000-0.1900.090-0.2320.1350.175-0.2430.015
calories0.5950.0590.0000.0240.0680.0990.068-0.1901.000-0.2950.9520.558-0.0880.947-0.010
deepSleepTime0.4500.0790.066-0.0610.0290.0220.0290.090-0.2951.000-0.296-0.2050.545-0.2960.048
distance0.8300.0730.010-0.0050.0470.0500.047-0.2320.952-0.2961.0000.550-0.1010.998-0.038
runDistance0.1700.0700.0400.1030.0520.0910.0520.1350.558-0.2050.5501.000-0.0200.557-0.045
shallowSleepTime0.2050.0640.061-0.0540.0240.0000.0240.175-0.0880.545-0.101-0.0201.000-0.1000.163
steps0.8150.0840.020-0.0050.0480.0580.048-0.2430.947-0.2960.9980.557-0.1001.000-0.041
wakeTime0.0600.0000.0220.0070.0000.0070.0000.015-0.0100.048-0.038-0.0450.163-0.0411.000

Missing values

2025-03-30T21:21:07.647918image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-30T21:21:08.318387image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Full_dateDay_NamestepsActivitydistancerunDistancecaloriesYearMonth NameDayTypeFull_date (Quarter)Full_date (Month Index)Full_date (Month)deepSleepTimeshallowSleepTimewakeTimestartstop
01-May-16Sunday3869Low265102442016MayWeekendQtr25May00014617080001461708000
18-May-16Sunday4088Low311901972016MayWeekendQtr25May158262214618012401461826560
29-May-16Monday1958Low126601342016MayWeekdayQtr25May234241014618835001461912000
311-May-16Wednesday2569Low166101982016MayWeekdayQtr25May239318014619795001462012920
413-May-16Friday4276Low280902512016MayWeekdayQtr25May180333314620658401462096800
514-May-16Saturday4591Low331402642016MayWeekendQtr25May5283014621472601462155360
615-May-16Sunday3164Low209401902016MayWeekendQtr25May1082871414622351001462259640
717-May-16Tuesday2499Low161501352016MayWeekdayQtr25May159237214623234201462347300
822-May-16Sunday1531Low99001192016MayWeekendQtr25May264409014624065801462446960
923-May-16Monday2585Low167101652016MayWeekdayQtr25May00014624856001462485600
Full_dateDay_NamestepsActivitydistancerunDistancecaloriesYearMonth NameDayTypeFull_date (Quarter)Full_date (Month Index)Full_date (Month)deepSleepTimeshallowSleepTimewakeTimestartstop
244425-Apr-22Monday7507Moderate52863231872022AprilWeekdayQtr24Apr11940702023-01-04 21:43:00+00002023-01-05 07:58:00+0000
244520-Jun-22Monday7069Moderate51193071842022JuneWeekdayQtr26Jun8818802023-01-06 01:40:00+00002023-01-06 07:12:00+0000
24461-Aug-22Monday5475Moderate416531011632022AugustWeekdayQtr38Aug9529802023-01-07 02:11:00+00002023-01-07 09:41:00+0000
244729-Aug-22Monday5028Moderate356028511822022AugustWeekdayQtr38Aug0002023-01-08 23:00:00+00002023-01-08 23:00:00+0000
244819-Sep-22Monday7598Moderate553143792612022SeptemberWeekdayQtr39Sep62175222023-01-09 04:01:00+00002023-01-09 08:54:00+0000
244910-Oct-22Monday6497Moderate473537272282022OctoberWeekdayQtr410Oct8526102023-01-10 01:29:00+00002023-01-10 08:05:00+0000
245017-Oct-22Monday8660Moderate618149542852022OctoberWeekdayQtr410Oct6025102023-01-11 02:14:00+00002023-01-11 08:29:00+0000
245124-Oct-22Monday8192Moderate590046325472022OctoberWeekdayQtr410Oct75183132023-01-12 02:15:00+00002023-01-12 08:25:00+0000
245221-Nov-22Monday6923Moderate470837352462022NovemberWeekdayQtr411Nov8625302023-01-13 00:02:00+00002023-01-13 07:07:00+0000
24532-Jan-23Monday8093Moderate579545042842023JanuaryWeekdayQtr11Jan110307112023-01-14 01:28:00+00002023-01-14 10:25:00+0000